Automatic construction of polygonal maps from point cloud data

Wiemann, Thomas; Nuchter, Andres; Lingemann, Kai; Stiene, Stefan; Hertzberg, Joachim

doi:10.1109/ssrr.2010.5981571

Cited by 18 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IV. A comparison of accuracy between 3D-NDT representation to grid-based [17] and triangle mesh based [16] representations was presented in [13]. Additionally in [10] we evaluated the covariance update against the one presented in [18].…”

Section: Tests and Resultsmentioning

confidence: 99%

“…Triebel et al [15] propose the Multi-Level Surface (MLS) map as an extension to elevation grids which allows for multiple height values to be stored per cell. Triangle meshes are another method for spatial representation popular in the computer graphics community (Wiemann et al [16]). …”

Section: Related Workmentioning

confidence: 99%

“…Stoyanov et al in [13] compared the accuracy of 3D-NDT representation to grid-based [17] and triangle mesh based [16] representations using extensive tests conducted in different environments. An important result of [13] was that 3D-NDT typically provides the best accuracy even at low resolutions.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

Saarinen

Stoyanov

Andreasson

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge, i.e., mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in highly dynamic 3D mapping and tracking scenario with centimeter accuracy over 1.5km trajectory.

show abstract

Section: Tests and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

Saarinen

Stoyanov

Andreasson

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…In (Carlberg et al, 2008), the surface reconstruction of a scan segment is preceded by a ground extraction step and combined with a mesh segmentation procedure based on a proximity criterion applied over the triangles. In (Wiemann et al, 2010) the polygonal map is exploited to perform floor, ceiling and wall labeling by clustering planar regions with similar attributes based on region growing principle. However, in cases where the reconstruction of the entire scene is not required, these approaches result in extra-computation for both steps, reconstructing non-ground objects and for eliminating them.…”

Section: Related and Proposed Reseach Workmentioning

confidence: 99%

Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

Craciun¹,

Morales²,

Deschaud³

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.

show abstract

“…Wiemann et al (2010) propose a simple approach to cope with low density regions by taking into account the shape of bounding boxes enclosing a neighbourhood. If the shape is elongated, with insufficient points, the neighbourhood has to be enlarged until the bounding box is square like.…”

Section: Neighbourhood Selectionmentioning

confidence: 99%

Scan matching for terrain mapping in open-pit mining

Aguirre¹,

Antonio²

View full text Add to dashboard Cite

This thesis is concerned with the problem of creating three-dimensional terrain maps using a highdensity, high-rate LiDAR sensor mounted on a moving platform without the use of an external localization solution. In particular, the thesis is motivated by the problem of building terrain maps in open-pit mining environments from data produced by haul-truck mounted LiDAR sensors.A foundation problem in map building from such data is the process of assembling together the point clouds generated by individual sensor scans into a common frame of reference. This falls into a broader class of well-studied problems called scan matching. Scan matching is most commonly solved by the Iterative Closet Point (ICP) method.This thesis makes two contributions. The first contribution is a comparison of the different ICP variants for the candidate application. The literature is replete with different algorithms that can be used at different stages of the ICP process. A population of 20,736 ICP-variants drawn from methods proposed in the literature is compared. Whilst others have looked to compare different ICP-variants, this investigation is distinguished from these earlier works in three respects: i) its comprehensiveness; ii) the focus of its target application (the loosely structured terrain of open-cut mining); and iii) its emphasis on how accuracy, precision, and computational efficiency trade-off across different variants. The main finding of this investigation is that the geometry of the point cloud critically determines the quality of the scan match. Significantly, of the variants considered, none were found to simultaneous meet requirements on accuracy, precision, and efficiency, highlighting the need for better approaches.The second contribution comes in the form of progress towards improving on the performance of established methods. For this, the thesis introduces the concept of "eigentropy" to quantify the geometric disorder or geometric information for points of a cloud. Eigentropy is conceptually similar to entropy as it appears in thermodynamics and information theory. Three novel methods are introduced to improve the performance of ICP-based scan-matching for loosely structured terrain. These methods are termed the: eigentropy filter, matching by normal deviation and unilateral eigentropy rejection, and they are based on the geometrical information content at a point. The results associated with these methods improve the overall performance of the ICP algorithm and five accurate, precise, efficient, and robust ICP variants are identified from an expanded population of 73,728. These five variants all use the methods introduced in this thesis.

show abstract

Automatic construction of polygonal maps from point cloud data

Cited by 18 publications

References 8 publications

Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

Scan matching for terrain mapping in open-pit mining

Contact Info

Product

Resources

About